Grid Based Cluster Head Selection Mechanism for Wireless sensor network

Haseeb, Khalid; Bakar, Kamalrulnizam Abu; Abdullah, Abdul Hanan; Ahmed, Adnan

doi:10.12928/telkomnika.v13i1.1190

Cited by 13 publications

(6 citation statements)

References 18 publications

(16 reference statements)

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“…The network encounters energy depletion faster during data transmission to the BS. Haseeb et al [13] presented a grid-based CHS technique through segmenting the field of the network into M × N dividers of uniform sizes that focus to reduce the dissipation of energy of sensors and enhancing network lifetime. This method works in a centralised fashion and requires details of the location of the network nodes, such that all nodes send their locations to the sink node.…”

Section: Normal Chs Methodsmentioning

confidence: 99%

Hybrid grey wolf sunflower optimisation algorithm for energy‐efficient cluster head selection in wireless sensor networks for lifetime enhancement

Nagarajan

Thangavelu

2020

IET Communications

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Wireless sensor networks (WSNs) are expected to find extensive applicability and accelerating deployment in the future. However, the main challenge faced in WSN is its perishing lifetime. The process of clustering a network is a popular mechanism employed for the purpose of extending the lifespan of WSNs and thereby making efficient data transmission. The main aim of a clustering algorithm is to elect an optimal cluster head (CH). The recent research trend suggests meta‐heuristic algorithms for the selection of optimal CHs. Meta‐heuristic algorithms possess the advantages of being simple, flexible, derivation‐free, and avoids local optima. This research proposes a novel hybrid grey wolf optimiser‐based sunflower optimisation (HGWSFO) algorithm for optimal CH selection (CHS) under certain factor constraints such as energy spent and separation distance, such that the network lifetime is enhanced. Sunflower optimisation (SFO) is employed for a broader search (exploration) where the variation of the step‐size parameter brings the plant closer to the sun in search of global refinement, thus increasing the exploration efficiency. Grey wolf optimisation (GWO) is employed for a narrow search (exploitation), where the parameter coefficient vectors are deliberately required to emphasise exploitation. This balances the exploration‐exploitation trade‐off, prolongs the network lifetime, increases the energy efficiency, and enhances the performance of the network with respect to overall throughput, residual energy of nodes, dead nodes, alive nodes, network survivability index, and convergence rate. The superior characteristic of the suggested HGWSFO is validated by comparing its performance with various other existing CHS algorithms. The overall performance of the proposed HGWSFO is 28.58%, 31.53%, 48.8%, 49.67%, 54.95%, 70.76%, and 87.10%, better than that of GWO, SFO, particle swarm optimisation (PSO), improved PSO, low‐energy adaptive clustering hierarchy (LEACH), LEACH‐centralised, and direct transmission, respectively.

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Section: Normal Chs Methodsmentioning

confidence: 99%

Hybrid grey wolf sunflower optimisation algorithm for energy‐efficient cluster head selection in wireless sensor networks for lifetime enhancement

Nagarajan

Thangavelu

2020

IET Communications

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“…The author [12] developed a fuzzy inductive reasoning, which was used to select a super CH from among CHs capable of transferring information to portable BS by selecting the optimal characteristics such as BS movement, residual electrical power, and clusters closeness. When data transfer to the BS, the system experiences great energy loss.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan

Devi¹,

Kuruba²,

Nam³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Wireless sensor networks (WSNs) are projected to have a wide range of applications in the future. The fundamental problem with WSN is that it has a finite lifespan. Clustering a network is a common strategy for increasing the lifetime of WSNs and, as a result, allowing for faster data transmission. The clustering algorithm's goal is to select the best cluster head (CH). In the existing system, Hybrid grey wolf sunflower optimization algorithm (HGWSFO)and optimal cluster head selection method is used. It does not provide better competence and output in the network. Therefore, the proposed Hybrid Grey Wolf Ant Colony Optimisation (HGWACO) algorithm is used for reducing the energy utilization and enhances the lifespan of the network. Black hole method is used for selecting the cluster heads (CHs). The ant colony optimization (ACO) technique is used to find the route among origin CH and destination. The open cache of nodes, transmission power, and proximity are used to improve the CH selection. The grey wolf optimisation (GWO) technique is the most recent and well-known optimiser module which deals with grey wolves' hunting activity (GWs). These GWs have the ability to track down and encircle food. The GWO method was inspired by this hunting habit. The proposed HGWACO improves the duration of the network, minimizes the power consumption, also it works with the large-scale networks.The HGWACO method achieves 25.64% of residual energy, 25.64% of alive nodes, 40.65% of dead nodes also it enhances the lifetime of the network.

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“…Grid based cluster-head selection (GBCHS) is presented in [69] to elect the CH among new deployed nodes while clusters are formed based on data transmission. The proposed algorithm works in centralized manner and needs global information of the network about location.…”

Section: Taxonomy Of Cluster-based Wsn Schemesmentioning

confidence: 99%

Clustering Analysis in Wireless Sensor Networks: The Ambit of Performance Metrics and Schemes Taxonomy

Zeb

Islam

Zareei

et al. 2016

International Journal of Distributed Sensor Networks

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Research on wireless sensor network (WSN) has increased tremendously throughout the years. In WSN, sensor nodes are deployed to operate autonomously in remote environments. Depending on the network orientation, WSN can be of two types: flat network and hierarchical or cluster-based network. Various advantages of cluster-based WSN are energy efficiency, better network communication, efficient topology management, minimized delay, and so forth. Consequently, clustering has become a key research area in WSN. Different approaches for WSN, using cluster concepts, have been proposed. The objective of this paper is to review and analyze the latest prominent cluster-based WSN algorithms using various measurement parameters. In this paper, unique performance metrics are designed which efficiently evaluate prominent clustering schemes. Moreover, we also develop taxonomy for the classification of the clustering schemes. Based on performance metrics, quantitative and qualitative analyses are performed to compare the advantages and disadvantages of the algorithms. Finally, we also put forward open research issues in the development of low cost, scalable, robust clustering schemes.

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Grid Based Cluster Head Selection Mechanism for Wireless sensor network

Cited by 13 publications

References 18 publications

Hybrid grey wolf sunflower optimisation algorithm for energy‐efficient cluster head selection in wireless sensor networks for lifetime enhancement

Hybrid grey wolf sunflower optimisation algorithm for energy‐efficient cluster head selection in wireless sensor networks for lifetime enhancement

Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan

Clustering Analysis in Wireless Sensor Networks: The Ambit of Performance Metrics and Schemes Taxonomy

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